This paper deals with a-posteriori error estimates for piecewise linear finite element approximations of parabolic problems in two space dimensions. The analysis extends previous results for elliptic problems to the p...This paper deals with a-posteriori error estimates for piecewise linear finite element approximations of parabolic problems in two space dimensions. The analysis extends previous results for elliptic problems to the parabolic context.展开更多
Spatially Constrained Mixture Model(SCMM)is an image segmentation model that works over the framework of maximum a-posteriori and Markov Random Field(MAP-MRF).It developed its own maximization step to be used within t...Spatially Constrained Mixture Model(SCMM)is an image segmentation model that works over the framework of maximum a-posteriori and Markov Random Field(MAP-MRF).It developed its own maximization step to be used within this framework.This research has proposed an improvement in the SCMM’s maximization step for segmenting simulated brain Magnetic Resonance Images(MRIs).The improved model is named as the Weighted Spatially Constrained Finite Mixture Model(WSCFMM).To compare the performance of SCMM and WSCFMM,simulated T1-Weighted normal MRIs were segmented.A region of interest(ROI)was extracted from segmented images.The similarity level between the extracted ROI and the ground truth(GT)was found by using the Jaccard and Dice similarity measuring method.According to the Jaccard similarity measuring method,WSCFMM showed an overall improvement of 4.72%,whereas the Dice similarity measuring method provided an overall improvement of 2.65%against the SCMM.Besides,WSCFMM signicantly stabilized and reduced the execution time by showing an improvement of 83.71%.The study concludes that WSCFMM is a stable model and performs better as compared to the SCMM in noisy and noise-free environments.展开更多
This article describes a local error estimator for Glimm＇s scheme for hyperbolic systems of conservation laws and uses it to replace the usual random choice in Glimm＇s scheme by an optimal choice. As a by-product of...This article describes a local error estimator for Glimm＇s scheme for hyperbolic systems of conservation laws and uses it to replace the usual random choice in Glimm＇s scheme by an optimal choice. As a by-product of the local error estimator, the procedure provides a global error estimator that is shown numerically to be a very accurate estimate of the error in L1 （R） for all times. Although there is partial mathematical evidence for the error estimator proposed, at this stage the error estimator must be considered ad- hoc. Nonetheless, the error estimator is simple to compute, relatively inexpensive, without adjustable parameters and at least as accurate as other existing error estimators. Numerical experiments in 1-D for Burgers＇ equation and for Euler＇s system are performed to measure the asymptotic accuracy of the resulting scheme and of the error estimator.展开更多
The goal of this paper is to present a versatile framework for solution verification of PDE＇s. We first generalize the Richardson Extrapolation technique to an optimized extrapolation solution procedure that construc...The goal of this paper is to present a versatile framework for solution verification of PDE＇s. We first generalize the Richardson Extrapolation technique to an optimized extrapolation solution procedure that constructs the best consistent solution from a set of two or three coarse grid solution in the discrete norm of choice. This technique generalizes the Least Square Extrapolation method introduced by one of the author and W. Shyy. We second establish the conditioning number of the problem in a reduced space that approximates the main feature of the numerical solution thanks to a sensitivity analysis. Overall our method produces an a posteriori error estimation in this reduced space of approximation. The key feature of our method is that our construction does not require an internal knowledge of the software neither the source code that produces the solution to be verified. It can be applied in principle as a postprocessing procedure to off the shelf commercial code. We demonstrate the robustness of our method with two steady problems that are separately an incompressible back step flow test case and a heat transfer problem for a battery. Our error estimate might be ultimately verified with a near by manufactured solution. While our pro- cedure is systematic and requires numerous computation of residuals, one can take advantage of distributed computing to get quickly the error estimate.展开更多
在基于隐马尔可夫模型的语音合成说话人自适应中,通常的最大似然线性回归(Maximum likelihoad linear regression,MLLR)方法在自适应后的音质和相似度等方面与原始语音仍有一定的差距。为了改善说话人自适应的效果,本文从识别的理论出发...在基于隐马尔可夫模型的语音合成说话人自适应中,通常的最大似然线性回归(Maximum likelihoad linear regression,MLLR)方法在自适应后的音质和相似度等方面与原始语音仍有一定的差距。为了改善说话人自适应的效果,本文从识别的理论出发,将结构化最大后验概率准则(Structure maximum aposteriori probability,SMAP)应用到语音合成的说话人自适应中,并将MLLR,MAP,SMAP等方法结合使用。通过一系列对参数、数据选取等实验,本文探讨了在语音合成中如何更好地提高说话人自适应后的音质和相似度。实验表明,在结合使用最大后验概率相关准则后,说话人自适应可以取得比MLLR更好的效果。展开更多
In this paper, local a priori, local a posteriori and global a posteriori error estimates are obtained for TQC9 element for the biharmonic equation. An adaptive algorithm is given based on the a posteriori error estim...In this paper, local a priori, local a posteriori and global a posteriori error estimates are obtained for TQC9 element for the biharmonic equation. An adaptive algorithm is given based on the a posteriori error estimates.展开更多
In recent years,the eigenvoice approach has proven to be an efficient method for rapid speaker adaptation,which directs the adaptation according to the analysis of full speaker vector space.In this article,we develope...In recent years,the eigenvoice approach has proven to be an efficient method for rapid speaker adaptation,which directs the adaptation according to the analysis of full speaker vector space.In this article,we developed a new algorithm for eigenspace-based adaptation restricting eigenvoices in clustered subspaces,and maximum likelihood(ML)criterion was replaced with maximum aposteriori(MAP)criterion for better parameter estimation.Experiments show that even with one sentence adaptation data this algorithm would result in 6.45%error ratio reduction relatively,which overcomes the instability of maximum likelihood linear regression(MLLR)with limited data and is much faster than traditional MAP method.This algorithm is not highly-dependent on subspace number of division,thus it proved to be a robust adaptation algorithm.展开更多
文摘This paper deals with a-posteriori error estimates for piecewise linear finite element approximations of parabolic problems in two space dimensions. The analysis extends previous results for elliptic problems to the parabolic context.
文摘Spatially Constrained Mixture Model(SCMM)is an image segmentation model that works over the framework of maximum a-posteriori and Markov Random Field(MAP-MRF).It developed its own maximization step to be used within this framework.This research has proposed an improvement in the SCMM’s maximization step for segmenting simulated brain Magnetic Resonance Images(MRIs).The improved model is named as the Weighted Spatially Constrained Finite Mixture Model(WSCFMM).To compare the performance of SCMM and WSCFMM,simulated T1-Weighted normal MRIs were segmented.A region of interest(ROI)was extracted from segmented images.The similarity level between the extracted ROI and the ground truth(GT)was found by using the Jaccard and Dice similarity measuring method.According to the Jaccard similarity measuring method,WSCFMM showed an overall improvement of 4.72%,whereas the Dice similarity measuring method provided an overall improvement of 2.65%against the SCMM.Besides,WSCFMM signicantly stabilized and reduced the execution time by showing an improvement of 83.71%.The study concludes that WSCFMM is a stable model and performs better as compared to the SCMM in noisy and noise-free environments.
基金supported by a Korea Research Foundation Grant from the Korean Government(MOEHRD)(KRF-2007-331-C00053)supported by the National Science and Engineering Council of Canada and the Canadian Foundation for Innovation
文摘This article describes a local error estimator for Glimm＇s scheme for hyperbolic systems of conservation laws and uses it to replace the usual random choice in Glimm＇s scheme by an optimal choice. As a by-product of the local error estimator, the procedure provides a global error estimator that is shown numerically to be a very accurate estimate of the error in L1 （R） for all times. Although there is partial mathematical evidence for the error estimator proposed, at this stage the error estimator must be considered ad- hoc. Nonetheless, the error estimator is simple to compute, relatively inexpensive, without adjustable parameters and at least as accurate as other existing error estimators. Numerical experiments in 1-D for Burgers＇ equation and for Euler＇s system are performed to measure the asymptotic accuracy of the resulting scheme and of the error estimator.
基金Sandia Nat.Lab.Sandia is a multiprogram laboratory operated by Sandia Corporation,a Lockheed Martin Company,for the United States Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000
文摘The goal of this paper is to present a versatile framework for solution verification of PDE＇s. We first generalize the Richardson Extrapolation technique to an optimized extrapolation solution procedure that constructs the best consistent solution from a set of two or three coarse grid solution in the discrete norm of choice. This technique generalizes the Least Square Extrapolation method introduced by one of the author and W. Shyy. We second establish the conditioning number of the problem in a reduced space that approximates the main feature of the numerical solution thanks to a sensitivity analysis. Overall our method produces an a posteriori error estimation in this reduced space of approximation. The key feature of our method is that our construction does not require an internal knowledge of the software neither the source code that produces the solution to be verified. It can be applied in principle as a postprocessing procedure to off the shelf commercial code. We demonstrate the robustness of our method with two steady problems that are separately an incompressible back step flow test case and a heat transfer problem for a battery. Our error estimate might be ultimately verified with a near by manufactured solution. While our pro- cedure is systematic and requires numerous computation of residuals, one can take advantage of distributed computing to get quickly the error estimate.
文摘在基于隐马尔可夫模型的语音合成说话人自适应中,通常的最大似然线性回归(Maximum likelihoad linear regression,MLLR)方法在自适应后的音质和相似度等方面与原始语音仍有一定的差距。为了改善说话人自适应的效果,本文从识别的理论出发,将结构化最大后验概率准则(Structure maximum aposteriori probability,SMAP)应用到语音合成的说话人自适应中,并将MLLR,MAP,SMAP等方法结合使用。通过一系列对参数、数据选取等实验,本文探讨了在语音合成中如何更好地提高说话人自适应后的音质和相似度。实验表明,在结合使用最大后验概率相关准则后,说话人自适应可以取得比MLLR更好的效果。
基金supported by the National Natural Science Foundation of China (10571006) and RFDP of China
文摘In this paper, local a priori, local a posteriori and global a posteriori error estimates are obtained for TQC9 element for the biharmonic equation. An adaptive algorithm is given based on the a posteriori error estimates.
文摘In recent years,the eigenvoice approach has proven to be an efficient method for rapid speaker adaptation,which directs the adaptation according to the analysis of full speaker vector space.In this article,we developed a new algorithm for eigenspace-based adaptation restricting eigenvoices in clustered subspaces,and maximum likelihood(ML)criterion was replaced with maximum aposteriori(MAP)criterion for better parameter estimation.Experiments show that even with one sentence adaptation data this algorithm would result in 6.45%error ratio reduction relatively,which overcomes the instability of maximum likelihood linear regression(MLLR)with limited data and is much faster than traditional MAP method.This algorithm is not highly-dependent on subspace number of division,thus it proved to be a robust adaptation algorithm.
